1. Technical Field
The present invention relates generally to capturing diagnostic and treatment information for individual diagnosis-treatment cycles and in particular to capturing such diagnostic and treatment information in a form suitable for effective analysis across multiple diagnosis-treatment cycle instances and providing guidance to a health care provider at the point of decision in a subsequent diagnosis-treatment cycle. Still more particularly, the present invention relates to a novel data structure capturing cost information, protocol treatment choices and rationales together with initial disease variable values and outcomes to permit both effective analysis and development of treatment guidelines. Codes may be effectively transferred onto the superbill and may be employed to facilitate or bypass the authorization process for insurance companies. Codes may provide an effective means of transferring data between dissimilar health and billing information systems, and for documenting the health care process to facilitate regulatory guideline compliance.
2. Description of the Related Art
Allocation of health care resources to individuals in a cost effective manner without compromise to outcomes and quality has become a significant issue in contemporary society. A movement exists to establish standards of care to assure that the highest quality of medicine is practiced in a uniform manner. These standards of care may include written protocols and practice guidelines or priority and appropriateness rankings promulgated by organizations, and/or priorities of diagnostics and treatment to be followed by individual health care providers. To successfully establish standards of care, however, diagnostic and treatment information must be both successfully captured in a form suitable for effective analysis and provided to the health care provider at the point of decision.
The capture of diagnostic and treatment information is impeded by the extreme degree of complexity associated with outcome data and their measurement and reliability. While theoretical models attempt to simplify the measurement tools for outcome analysis, outcomes are not simply xe2x80x9ccuredxe2x80x9d versus xe2x80x9cnot curedxe2x80x9d propositions, but instead include variables driven by issues such as quality of life, increased longevity, complications, and side effects. To compensate, some methodologies factor such variables into the outcome measurement to derive xe2x80x9cquality adjustedxe2x80x9d results. This factoring makes it difficult to formulate specific recommendations for individual cases.
Currently, ICD9 codes, which are general descriptions of the disease process, and CPT and DRG billing codes are the only information typically available for analysis of individual diagnostic-treatment cycles. Attempts to retrospectively obtain data necessary for effective analysis, such as the rationale for a particular treatment choice, is extremely difficult since such information is not normally captured. Thus medical societies, which typically gather only measurement data, and the insurance industry, which is substantially constrained to analyzing information provided with billing records, are generally unable to obtain this information for analysis.
Early attempts at an Electronic Medical Record have taken the form of simply converting the paper chart to a paperless chart contained in a medical electronic medical record database. Since much of the record is in text form, analysis of clinical data is hampered by inconsistent data entry, the absence of relationships between the data collected, and the lack of consistent vocabularies allowing comparison between and among systems. Consistent data fields are largely demographic in nature rather than oriented to clinical research. While the need for consistent database fields to support data analysis has been recently recognized, and some medical societies are developing outcome study databases for the relevant specialty, no effort has been undertaken to capture specific and accurate clinical and cost information for diagnostics and treatments based on specific disease issues. Such information is necessary for effective analysis both within specialties and globally across all specialties. Clinical and costs analyses of outcome data would benefit both the health care profession and insurance providers.
For effective use, clinical and cost information from prior diagnostic-treatment cycles must also be provided to health care professionals at the point of decision. Customary practices are difficult to influence or alter without the ability to offer suggestions at the time the customary practice is performed.
Additionally, there are no current mechanisms in place to check CPT billing codes for inaccuracy and abuse, other than random individual hand chart reviews, which may be both tedious and erratic and is impossible to perform with any significant volume of diagnostic-treatment information.
It would be desirable, therefore, to provide a data structure for capturing diagnostic-treatment information for effective analysis and for guidance of health care providers at the point of decision. Further, the paramount need for CPT code inaccuracy and abuse detection is satisfied. The availability in the present invention of disease and protocol variables for cross-matching with CPT code variables permits significant analysis and filtering of CPT codes.
A diagnostic and treatment information data structure includes: a disease variable code documenting the variables for a disease process which are important to a particular medical specialty; an optional protocol grouping code identifying priorities assigned by a specialty-specific medical society or other organization to available diag-nostic and treatment protocol choices base on measured disease variable values; a protocol choice code identifying the diagnostic and treatment regime selected by the health care provider, preferably integrated into a justification code identifying a rationale advanced by the health care provider for choosing the selected protocol choice; a diagnostic/treatment justification code for each procedure, diagnostic study, and treatment ordered for the disease process, containing the rationale of the health care provider in ordering the procedure, study or treatment and the priority assigned to the procedure, study or treatment by the medical societies; and a CPT variable code identifying billing for procedures which may be cross-correlated and checked against the disease variables, protocol choice, and diagnostic/treatment justifications. The diagnostic and treatment information data structure thus encapsulates, without identifying a specific patient, information regarding a particular diagnosis-treatment cycle for an individual patient. The diagnostic and treatment information data structures for a number of diagnosis-treatment cycle may be combined within a database for analysis in outcomes or cost effectiveness studies. A relational database which assists the health care provider in formulating the diagnostic and treatment information data structure for a specific diagnosis-treatment cycle may, within a user interface, display information determined during the outcomes or cost effectiveness studies to influence the health care provider at the point of decision, and may serve to satisfy the documentation requirements being mandated by regulatory organizations. Effective analyses of diagnostic, treatment, and outcomes information and guidance for health care professionals based on such analyses is thus facilitated. An Internet/intranet database program employing the diagnostic and treatment information data structure contains both clinical and financial information permitting effective filtering of CPT codes as to accuracy and appropriateness.